Home Clinical • Download PDF by Peter J. Schüffler, Dwarikanath Mahapatra, Jeroen A. W.: Abdominal Imaging. Computation and Clinical Applications:

Download PDF by Peter J. Schüffler, Dwarikanath Mahapatra, Jeroen A. W.: Abdominal Imaging. Computation and Clinical Applications:

By Peter J. Schüffler, Dwarikanath Mahapatra, Jeroen A. W. Tielbeek, Franciscus M. Vos (auth.), Hiroyuki Yoshida, Simon Warfield, Michael W. Vannier (eds.)

ISBN-10: 3642410820

ISBN-13: 9783642410826

ISBN-10: 3642410839

ISBN-13: 9783642410833

This publication constitutes the refereed complaints of the fifth foreign Workshop CCAA 2013, held along with MICCAI 2013, in Nagoya, Japan, in September 2013. The e-book contains 32 papers that have been rigorously reviewed and chosen from 38 submissions. the themes lined are belly atlases, form research and morphology in belly buildings and organs, detection of anatomical and practical landmarks, dynamic, useful, physiologic, and anatomical stomach imaging, registration equipment for stomach intra- and inter-patient variability, augmented fact ideas for intervention, medical purposes in radio-frequency ablation, open surgical procedure, and minimally invasive surgery.

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Extra resources for Abdominal Imaging. Computation and Clinical Applications: 5th International Workshop, Held in Conjunction with MICCAI 2013, Nagoya, Japan, September 22, 2013. Proceedings

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The cylindrical images show the shape index intensities of the source (top) and target (bottom) colon surfaces. The marked region is enlarged for better visibility. 5 Re-establishing Correspondence on the Updated Surfaces After applying the TPS deformation the source surface resembles the target surface more closely. In Fig. 6 a source surface is being deformed using TPS and mapped into the 3D space of the target image. Good overlap can be seen in the colonic segments where previously a good spatial correspondence was detected using the local similarity measure S (segment between arrows marked on the right colon in Fig.

We propose a self similarity registration method based on the Hessian (HE) that efficiently deals with the recalculation issue. The representation of the local self-similarity via the Hessian enables keeping it up to date during deformation. As such, the registration procedure is efficient and not prone to fall in local minima. 05) over leaving the reorientation out. Our technique also has a better performance over the existing MIND method on the DIR-Lab dataset as well as on abdominal MRI datasets albeit not significant.

2 Definition of the Hessian and Reorientation Strategy An alternative method to measure local structure is based on the Hessian (HE): ⎡ ⎤ Ixx Ixy Ixz HE = ⎣ Iyx Iyy Iyz ⎦ (4) Izx Izy Izz The calculation of image derivatives is based Gaussian kernals (width: σg ). Typically, the HE is sensitive to the linelike structures (even structures). It captures local orientaion which is also covered by MIND. In Diffusion Tensor Image (DTI) registration, tensor reorientation has been incorporated in the registration optimization [14].

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Abdominal Imaging. Computation and Clinical Applications: 5th International Workshop, Held in Conjunction with MICCAI 2013, Nagoya, Japan, September 22, 2013. Proceedings by Peter J. Schüffler, Dwarikanath Mahapatra, Jeroen A. W. Tielbeek, Franciscus M. Vos (auth.), Hiroyuki Yoshida, Simon Warfield, Michael W. Vannier (eds.)


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